Revista de Economia e Sociologia Rural
https://revistasober.org/article/doi/10.1590/1806-9479.2023.276680
Revista de Economia e Sociologia Rural
ORIGINAL ARTICLE

Efficiency and productivity to social welfare: the case of the main forestry-producing micro-regions in Brazil

Eficiência e produtividade do bem-estar social: o caso das principais microrregiões florestais do Brasil

Jessica Suarez Campoli; Paulo Nocera Alves Junior; Tatiana Kimura Kodama; Marcelo Seido Nagano; Heloisa Lee Burnquist

http://dx.doi.org/10.1590/1806-9479.2023.276680 resr, vol.62, n4, e276680, 2024
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Abstract

The studies on the forest sector focus on energy issues and environmental challenges, but they are limited to a small number of studies focused on economic growth and social welfare. In the forest sector, Brazil is among the five countries with large forest cover in the world, with favorable conditions and great potential for production growth. Therefore, this work aimed to measure the evolution of efficiency and productivity of the 49 Brazilian forestry microregions in converting the expansion of economic growth into social welfare from 2009 to 2015 (a period of sectoral growth in the country). The approach of the Slack-Based Measure (SBM) – Data Envelopment Analysis (DEA), Malmquist Productivity Index (MPI), and Windows Analysis model was combined, followed by a solution for infeasibility problems. The results show that the growth of the forestry sector was not accompanied by the Human Development Index (HDI) in most of the microregions, showing regional and state differences, with the microregions close to the sensitive environmental areas with the lowest HDI. Thus, the work contributes to the design of public policies and government decision-making to increase the sector's efficiency and productivity and to social indicators that can guide sustainable policies in other contexts and countries.

Keywords

forestry sector, social welfare, rural development, Data Envelopment Analysis (DEA), Malmquist Productivity Index (MPI)

Resumo

Resumo: Os estudos sobre o setor florestal concentram-se em questões energéticas e desafios ambientais o que reflete em um número limitado de estudos focados no crescimento econômico e no bem-estar social. No setor florestal, o Brasil está entre os cinco países com a maior cobertura florestal do mundo com condições favoráveis e grande potencial de crescimento da produção. Portanto, o objetivo deste trabalho foi mensurar a evolução da eficiência e produtividade das 49 microrregiões florestais brasileiras em converter a expansão do crescimento econômico em bem-estar social, de 2009 a 2015 (período de crescimento setorial no país). Combinou-se a abordagem do modelo Slack-Based Measure (SBM) – Data Envelopment Analysis (DEA), Malmquist Productivity Index (MPI) e Windows Analysis, seguido de uma solução para problemas de inviabilidade. Os resultados obtidos apontam que o crescimento do setor florestal não foi acompanhado pelo Índice de Desenvolvimento Humano (IDH) na maioria das microrregiões apresentando grandes diferenças regionais e estaduais sendo as microrregiões próximas as áreas ambientais sensíveis com o menor IDH. Assim, o trabalho contribui para o desenho de políticas públicas e tomadas de decisões governamentais para aumentar a eficiência e produtividade do setor e para indicadores sociais podendo orientar políticas sustentáveis em outros contextos e países.

Palavras-chave

setor florestal, bem-estar social, desenvolvimento rural, Data Envelopment Analysis (DEA), Malmquist Productivity Index (MPI)

Referências

Ahn, H., Afsharian, M., Emrouznejad, A., & Banker, R. (2018). Recent developments on the use of DEA in the public sector. Socio-Economic Planning Sciences, 61, 1-3.

Associação Brasileira Técnica de Celulose e Papel – ABTCP. (2023). O setor. Retrieved in 2023, July 17, from https://www.abtcp.org.br/o-setor

Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078-1092.

Brasil. Ministério da Agricultura e Pecuária. (2018). Plano nacional de desenvolvimento de florestas plantadas. Retrieved in 2023, July 17, from https://www.gov.br/agricultura/pt-br/assuntos/politica-agricola/outras-publicacoes/plano-nacional-de-desenvolvimento-de-florestas-plantadas.pdf/view

Brasil. Sistema Nacional de Informações Florestais – SNIF. (2021). Série histórica das exportações (PFM): tabelas e gráficos. Retrieved in 2023, July 17, from https://snif.florestal.gov.br/pt-br/emprego/146-producao-economia-e-mercado-florestal/dados-complementares-exportacao-produtos-florestais-madeireiros/332-serie-historica-das-exportacoes-pfm-tabelas-e-graficos

Camioto, F. D. C., & Pulita, A. C. (2022). Efficiency evaluation of sustainable development in BRICS and G7 countries: a Data Envelopment Analysis approach. Gestão & Produção, 29, e022.

Camioto, F. D. C., Mariano, E. B., & Rebelatto, D. A. (2017). Sustainability improvement opportunities in Brazilian sectors: analysis of DEA slacks. Brazilian Journal of Operations & Production Management, 14(3), 363-370.

Campoli, J. S., & Stivali, M. (2023). Custo social do desmatamento nos biomas brasileiros. Brasília: IPEA.

Campoli, J. S., Alves Junior, P. N., Kodama, T. K., Nagano, M. S., & Burnquist, H. L. (2024a). G20 countries’ progress on the 7th SDG under circular economy DEA model. Environmental Science & Policy, 160, 103839.

Campoli, J. S., Kodama, T. K., Nagano, M. S., & Burnquist, H. L. (2024b). Advancing circular economy: G20 nations’ path towards 12th sustainable development goal. Circular Economy and Sustainability, 1-24. In press.

Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444.

[REMOVED IF= FIELD]Charnes, A., Cooper, W. W., Lewin, A. Y. and Seiford, L. M. (1994). Data envelopment analysis: theory, methodology and applications. Springer.

Chen, J.-X., Deng, M., & Gingras, S. (2011). A modified super-efficiency measure based on simultaneous input–output projection in data envelopment analysis. Computers & Operations Research, 38(2), 496-504.

Chen, Y., & Liang, L. (2011). Super-efficiency DEA in the presence of infeasibility: one model approach. European Journal of Operational Research, 213(1), 359-360.

Chen, Y., Su, X., & Hipel, K. W. (2009). An index aggregation approach to comparing the overall performance of emerging and developed countries. Socio-Economic Planning Sciences, 43(1), 25-39.

Coelho, M. R. F., & Coelho, M. H. (2013). Panorama da indústria de celulose e papel no Brasil: 2001 a 2011. Floresta, 43(3), 463-474.

Coelli, T. J., Rao, D. S. P., O’Donnell, C. J., & Battese, G. E. (2005). An introduction to efficiency and productivity analysis. New York: Springer Science & Business Media.

Cook, W. D., Tone, K., & Zhu, J. (2014). Data envelopment analysis: prior to choosing a model. Omega, 44, 1-4.

Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis: a comprehensive text with models, applications, references and DEA-solver software. New York: Springer.

Dyckhoff, H., & Souren, R. (2020). Data envelopment methodology of performance evaluation. In H. Dyckhoff & R. Souren (Eds.), Performance evaluation: foundations and challenges (pp. 47-82). Cham: Springer International Publishing.

Fang, H.-H., Lee, H.-S., Hwang, S.-N., & Chung, C.-C. (2013). A slacks-based measure of super-efficiency in data envelopment analysis: an alternative approach. Omega, 41(4), 731-734.

Fare, R., & Grosskopf, S. (1992). Malmquist productivity indexes and fisher ideal indexes. Economic Journal, 102(410), 158-160.

Fare, R., Grosskopf, S., Norris, M., & Zhang, Z. (1994). Productivity growth, technical progress, and efficiency change in industrialized countries. The American Economic Review, 84, 66-83.

Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253-290.

Ferreira, C. M. C., & Gomes, A. P. (2020). Introdução à análise envoltória de dados: teoria, modelos e aplicação (2ª ed.). Viçosa: Editora da UFV.

Firjan. (2021). IFDM: Índice FIRJAN de Desenvolvimento Municipal: consulta. Retrieved in 2023, July 17, from https://www.firjan.com.br/ifdm/

Food and Agriculture Organization – FAO. (2021). FAOSTAT: forest production and trade. Rome. Retrieved in 2023, July 17, from http://www.fao.org/faostat/en/#data/FO

Gandour, C., Diego, M., João Pedro, V., & Assunção, J. J. (2021). Degradação Florestal na Amazônia: fenômeno relacionado ao desmatamento precisa ser alvo de política pública. Rio de Janeiro: Climate Policy Initiative.

Hou, J., Ruan, X., Lv, J., & Guo, H. (2020). Two-stage super-efficiency slacks-based model to assess China’s ecological wellbeing. International Journal of Environmental Research and Public Health, 17(19), 7045.

Indústria Brasileira de Árvores – IBÁ. (2017). Report 2017. Brasília.

Indústria Brasileira de Árvores – IBÁ. (2019). Report 2019. Brasília.

Indústria Brasileira de Árvores – IBÁ. (2022). Report 2022. Brasília.

Instituto Brasileiro de Geografia e Estatística – IBGE. (2021a). Average wood cost (BRL/m3) and employment in forestry and paper and pulp production. Retrieved in 2023, July 17, from https://www.ibge.gov.br/estatisticas/economicas/precos-e-custos/9270-sistema-nacional-de-pesquisa-de-custos-e-indices-da-construcao-civil.html?=&t=o-que-e

Instituto Brasileiro de Geografia e Estatística – IBGE. (2021b). GDP. Retrieved in 2023, July 17, from https://www.ibge.gov.br/explica/pib.php

Instituto Brasileiro de Geografia e Estatística – IBGE. (2021c). Total number of employed people. Retrieved in 2023, July 17, from https://www.ibge.gov.br/estatisticas/sociais/trabalho.html

Instituto Nacional de Pesquisas Espaciais – INPE. (2021). Terrabrasilis: geographic data platform. Retrieved in 2023, July 17, from http://terrabrasilis.dpi.inpe.br/

Kourtit, K., Nijkamp, P., & Suzuki, S. (2020). Are global cities sustainability champions? A double delinking analysis of environmental performance of urban agglomerations. The Science of the Total Environment, 709, 134963.

Lábaj, M., Luptáčik, M., & Nežinský, E. (2014). Data envelopment analysis for measuring economic growth in terms of welfare beyond GDP. Empirica, 41(3), 407-424.

Lara, C. S., Costa, C. R., & Sampaio, P. T. B. (2021). O mercado de sementes e mudas de pau-rosa (Aniba spp.) no Estado do Amazonas. Revista de Economia e Sociologia Rural, 59(3), e221035.

Leite-Filho, A. T., Soares-Filho, B. S., Davis, J. L., Abrahão, G. M., & Börner, J. (2021). Deforestation reduces rainfall and agricultural revenues in the Brazilian Amazon. Nature Communications, 12(1), 2591.

Liu, T., Li, J., Chen, J., & Yang, S. (2019). Urban ecological efficiency and its influencing factors: a case study in Henan province, China. Sustainability, 11(18), 5048.

Long, R., Ouyang, H., & Guo, H. (2020). Super-slack-based measuring data envelopment analysis on the spatial–temporal patterns of logistics ecological efficiency using global Malmquist Index model. Environmental Technology & Innovation, 18, 100770.

López, R., & Miller, S. J. (2008). Chile: the unbearable burden of inequality. World Development, 36(12), 2679-2695.

Lovejoy, T. E., & Nobre, C. (2018). Amazon tipping point. Science Advances, 4(2), eaat2340.

Mariano, E. B., & Rebelatto, D. A. (2014). Transformation of wealth produced into quality of life: Analysis of the social efficiency of nation-states with the DEA’s triple index approach. The Journal of the Operational Research Society, 65(11), 1664-1681.

Montebello, A. E. S., & Bacha, C. J. C. (2009). Avaliação das pesquisas e inovações tecnológicas ocorridas na silvicultura e na produção industrial de celulose no Brasil. Revista de Economia e Sociologia Rural, 47(2), 485-517.

Moralles, H. F., & Rebelatto, D. A. (2016). The effects and time lags of R&D spillovers in Brazil. Technology in Society, 47, 148-155.

Moreno‐Enguix, M. D. R., & Lorente Bayona, L. V. (2017). Factors affecting public expenditure efficiency in developed countries. Politics & Policy, 45(1), 105-143.

O’Neill, B. C., Kriegler, E., Ebi, K. L., Kemp-Benedict, E., Riahi, K., Rothman, D. S., van Ruijven, B. J., van Vuuren, D. P., Birkmann, J., Kok, K., Levy, M., & Solecki, W. (2017). The roads ahead: narratives for shared socioeconomic pathways describing world futures in the 21st century. Global Environmental Change, 42, 169-180.

O’Sullivan, A., & Sheffrin, S. M. (2006). Economics: principles in action. Upper Saddle River: Pearson Prentice Hall.

Observatory of Economic Complexity – OEC. (2021). Brazil (BRA) exports, imports, and trade partners. Retrieved in 2023, July 17, from https://oec.world/en/profile/country/bra

Ren, W., Zhang, Z., Wang, Y., Xue, B., & Chen, X. (2020). Measuring regional eco-efficiency in China (2003-2016): a “Full World” perspective and network data envelopment analysis. International Journal of Environmental Research and Public Health, 17(10), 3456.

Rentizelas, A., Melo, I. C., Alves Junior, P. N., Campoli, J. S., & Aparecida do Nascimento Rebelatto, D. (2019). Multi-criteria efficiency assessment of international biomass supply chain pathways using Data Envelopment Analysis. Journal of Cleaner Production, 237, 117690.

Robalino-López, A., Mena-Nieto, A., García-Ramos, J.-E., & Golpe, A. A. (2015). Studying the relationship between economic growth, CO2 emissions, and the environmental Kuznets curve in Venezuela (1980-2025). Renewable & Sustainable Energy Reviews, 41, 602-614.

Sarmento, J., Renneboog, L., & Verga Matos, P. (2017). Measuring highway efficiency by a DEA approach and the Malmquist index. European Journal of Transport and Infrastructure Research, 17(4), 530-551.

Selvatti, T. S. (2015). Concentração da produção e da exportação mundial de celulose e de Medium Density Fiberboard (MDF) (Dissertação de mestrado). Universidade Federal de Lavras, Lavras.

Shah, W. U. H., Hao, G., Yan, H., Shen, J., & Yasmeen, R. (2024). Forestry resource efficiency, total factor productivity change, and regional technological heterogeneity in China. Forests, 15(1), 152.

Song, Y., & Mei, D. (2022). Sustainable development of China’s regions from the perspective of ecological welfare performance: analysis based on GM (1, 1) and the Malmquist index. Environment, Development and Sustainability, 24(1), 1086-1115.

Sun, Y., & Wang, N. (2022). Evolution and obstacle factors of high-quality industrial development in the π-shaped Curve Area of the Yellow River basin in China. Journal of Geographical Sciences, 32(12), 2430-2452.

Thore, S., & Tarverdyan, R. (2009). Using data envelopment analysis to quantify ILO objectives and identify policies conducive to decent work in a globalizing world. Socio-Economic Planning Sciences, 43(3), 151-164.

Timber Trade Portal. (2023). Forest resources and context of Brazil. Retrieved in 2023, July 17, from https://www.timbertradeportal.com/en/brazil/10/country-context

Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130(3), 498-509.

Tone, K. (2002). A slacks-based measure of super-efficiency in data envelopment analysis. European Journal of Operational Research, 143(1), 32-41.

Tone, K. (2011). Slacks-based measure of efficiency. In W. W. Cooper, L. M. Seiford & J. Zhu (Eds.), Handbook on data envelopment analysis (pp. 195-209). Boston: Springer.

Tone, K. (2017). Advances in DEA theory and applications: with extensions to forecasting models. Hoboken: John Wiley & Sons.

Tone, K., & Tsutsui, M. (2017). Malmquist productivity index models. In K. Tone (Ed.), Advances in DEA theory and applications. Chichester: John Wiley & Sons.

Ülengin, F., Kabak, Ö., Önsel, S., Aktas, E., & Parker, B. R. (2011). The competitiveness of nations and implications for human development. Socio-Economic Planning Sciences, 45(1), 16-27.

Varella, T. S., Oliveira Neto, G. C., & Sousa, T. B. (2022). Adoption of cleaner production in a pupunha palm heart factory: a systematic literature review and a case study. Food Science and Technology, 42, e65720. http://doi.org/10.1590/fst.65720

Wong, S. Y. (2020). Assessment of public expenditure efficiency: a review. Journal of Economics and Sustainability, 2(2), 12.

World Integrated Trade Solutions – WITS. (2020). Data availability. Retrieved in 2023, July 17, from https://wits.worldbank.org/tariff/non-tariff-measures/en/ntm-dataavailability

Yang, Z., & Soltani, N. (2021). A new SBM-based global Malmquist productivity index and target setting. Computers & Industrial Engineering, 158, 107411.

Zhang, C., Li, J., Liu, T., Xu, M., Wang, H., & Li, X. (2022). The spatiotemporal evolution and influencing factors of the Chinese cities’ ecological welfare performance. International Journal of Environmental Research and Public Health, 19(19), 12955.

Zhu, J. (2001). Super-efficiency and DEA sensitivity analysis. European Journal of Operational Research, 129(2), 443-455.
 

Submetido em:
17/07/2023

Aceito em:
26/08/2024

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